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Found 11 papers, 4 papers with code
Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints
Finally, our experiments demonstrate the promise of our approach to an array of machine-learning problems that involve orthogonality constraints.
Symplectic eigenvalue problem via trace minimization and Riemannian optimization
We address the problem of computing the smallest symplectic eigenvalues and the corresponding eigenvectors of symmetric positive-definite matrices in the sense of Williamson's theorem.
Optimization and Control Spectral Theory 15A15, 15A18, 70G45
A Grassmann Manifold Handbook: Basic Geometry and Computational Aspects
The Grassmann manifold of linear subspaces is important for the mathematical modelling of a multitude of applications, ranging from problems in machine learning, computer vision and image processing to low-rank matrix optimization problems, dynamic low-rank decompositions and model reduction.
Numerical Analysis Numerical Analysis Differential Geometry 15-02, 15A16, 15A18, 15B10, 22E70, 51F25, 53C80, 53Z99
Alternating minimization algorithms for graph regularized tensor completion
Furthermore, based on the Kurdyka-{\L}ojasiewicz property, we show that the sequence generated by the proposed algorithm globally converges to a critical point of the objective function.
Assessment of COVID-19 hospitalization forecasts from a simplified SIR model
Moreover, we observed that, when the model is trained on a suitable three-week period around the first hospitalization peak for Belgium, it forecasts the subsequent two months with mean absolute percentage error (MAPE) under 4%.
Riemannian Optimization on the Symplectic Stiefel Manifold
The symplectic Stiefel manifold, denoted by $\mathrm{Sp}(2p, 2n)$, is the set of linear symplectic maps between the standard symplectic spaces $\mathbb{R}^{2p}$ and $\mathbb{R}^{2n}$.
Optimization and Control Dynamical Systems
On a minimum enclosing ball of a collection of linear subspaces
By scaling the objective and penalizing the information lost by the rank-$k$ minimax center, we jointly recover an optimal dimension, $k^*$, and a central subspace, $U^* \in$ Gr$(k^*, n)$ at the center of the minimum enclosing ball, that best represents the data.
VIP: Vortex Image Processing package for high-contrast direct imaging
We present the Vortex Image Processing (VIP) library, a python package dedicated to astronomical high-contrast imaging.
Instrumentation and Methods for Astrophysics
Proceedings of the third "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'16)
The third edition of the "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) took place in Aalborg, the 4th largest city in Denmark situated beautifully in the northern part of the country, from the 24th to 26th of August 2016.
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions.
Manopt, a Matlab toolbox for optimization on manifolds
Optimization on manifolds is a rapidly developing branch of nonlinear optimization.
